set_ZicoSeq: set_ZicoSeq

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

set_ZicoSeq

Description

Set the parameters for ZicoSeq differential abundance detection method.

Usage

set_ZicoSeq(
  assay_name = "counts",
  contrast = NULL,
  strata = NULL,
  adj.name = NULL,
  feature.dat.type = c("count", "proportion", "other"),
  is.winsor = TRUE,
  outlier.pct = 0.03,
  winsor.end = c("top", "bottom", "both"),
  is.post.sample = TRUE,
  post.sample.no = 25,
  perm.no = 99,
  link.func = list(function(x) sign(x) * (abs(x))^0.5),
  ref.pct = 0.5,
  stage.no = 6,
  excl.pct = 0.2,
  expand = TRUE
)

Arguments

assay_name	the name of the assay to extract from the TreeSummarizedExperiment object (default assayName = "counts"). Not used if the input object is a phyloseq.
contrast	character vector with exactly, three elements: a string indicating the name of factor whose levels are the conditions to be compared, the name of the level of interest, and the name of the other level.
strata	a factor such as subject IDs indicating the permutation strata or characters indicating the strata variable in meta.dat. Permutation will be confined to each stratum. This can be used for paired or some longitudinal designs.
adj.name	the name(s) for the variable(s) to be adjusted. Multiple variables are allowed. They could be numeric or categorical; should be in meta.dat.
feature.dat.type	the type of the feature data. It could be "count", "proportion" or "other". For "proportion" data type, posterior sampling will not be performed, but the reference-based ratio approach will still be used to address compositional effects. For "other" data type, neither posterior sampling or reference-base ratio approach will be used.
is.winsor	a logical value indicating whether winsorization should be performed to replace outliers. The default is TRUE.
outlier.pct	the expected percentage of outliers. These outliers will be winsorized. The default is 0.03. For count/proportion data, outlier.pct should be less than prev.filter.
winsor.end	a character indicating whether the outliers at the "top", "bottom" or "both" will be winsorized. The default is "top". If the feature.dat.type is "other", "both" may be considered.
is.post.sample	a logical value indicating whether to perform posterior sampling of the underlying proportions. Only relevant when the feature data are counts.
post.sample.no	the number of posterior samples if posterior sampling is used. The default is 25.
perm.no	the number of permutations. If the raw p values are of the major interest, set perm.no to at least 999.
link.func	a list of transformation functions for the feature data or the ratios. Based on our experience, square-root transformation is a robust choice for many datasets.
ref.pct	percentage of reference taxa. The default is 0.5.
stage.no	the number of stages if multiple-stage normalization is used. The default is 6.
excl.pct	the maximum percentage of significant features (nominal p-value < 0.05) in the reference set that should be removed. Only relevant when multiple-stage normalization is used.
expand	logical, if TRUE create all combinations of input parameters (default expand = TRUE).

Value

A named list containing the set of parameters for DA_ZicoSeq method.

See Also

DA_ZicoSeq

Examples

# Set some basic combinations of parameters for ZicoSeq
base_ZicoSeq <- set_ZicoSeq(contrast = c("group", "B", "A"), 
    feature.dat.type = "count", winsor.end = "top")
many_ZicoSeq <- set_ZicoSeq(contrast = c("group", "B", "A"), 
    feature.dat.type = "count", outlier.pct = c(0.03, 0.05),
    winsor.end = "top", is.post.sample = c(TRUE, FALSE))

mcalgaro93/benchdamic documentation built on Nov. 23, 2024, 5:06 p.m.